Prediction of Coke CSR from Ash Chemistry of Coal Blend

Nag, Debjani; Haldar, S. K.; Choudhary, Pradeep; Banerjee, P. K.

doi:10.1080/19392690903218117

Cited by 17 publications

(5 citation statements)

References 7 publications

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“…In the mathematical model described above, formula in Equation (16) is outlined as per the ash chemistry and proximate analysis (Nag et al ., 2009). CSR (24), Basic (20) and acidic (21) formula referenced from “ Influence of Geology on CSR ” Pearson's study (Unsal, 2018).…”

Section: Modelmentioning

confidence: 99%

“…Since CRI t can be calculated according to the value of CSR t , in our results, we only report the value of CSR t . Two further quality measures of secondary interest for the coke quality are MBI t and MMR t whose higher values are more desirable (Nag et al ., 2009; Unsal, 2018). For the two years' data collected between April 2017 and April 2019, the plant has been observing the CSR t value to be between 65% and 67%, and CRI t value between 22% and 24%.…”

Section: Case Studymentioning

confidence: 99%

“…To evaluate the coke quality, CSR t and CRI t are considered to be the most valuable parameters in which higher CSR t and lower CRI t are desirable and forms linear relation (Nag et al ., 2009; North et al ., 2018). Since CRI t can be calculated according to the value of CSR t , in our results, we only report the value of CSR t .…”

Section: Case Studymentioning

confidence: 99%

See 2 more Smart Citations

Dynamic coal blending under sustainability concerns: a case study from the Indian steel industry

Ahmad

Firouz²,

Srivastava³

2023

IJQRM

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PurposeIncreasing scarcity of natural resources and the adverse effects of unsustainable practices call for more and more efficient management strategies in the energy industry. The quality of the coke plays a significant role in the quality and durability of the output steel which is produced using the energy from the coal. This paper aims to investigate the dynamic coal blending problem under overall cost and coke quality constraints in the steel industry within a periodic cycle of operations.Design/methodology/approachConsidering the variability of the natural properties over a periodic cycle, this study proposes a multi-period mixed-integer non-linear programming formulation to optimize the total blending costs while taking various coke quality constraints into account. Besides, this study applies factorial design to investigate about the significant effect of coal proportions as well as improvement into the overall cost of blending.FindingsIn this case study, utilizing real data from a coal blending facility in India, through a factorial design, the authors obtain optimal desirable levels of coal proportions and their criticality levels towards the total cost of blending (TCB) or objective function. This analysis reflects the role of the coke quality constraints in the objective function value while characterizing the price of sustainability for the case study among other critical insights.Originality/valueObjective function (or TCB) includes basic coal cost, movement cost and environmental costs during the coal and coke processing at a coke-oven and blast furnace of steel industry. The price of sustainability provides managerial insights on that sacrifices the industry has to make in order to become more “sustainable”.

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Section: Modelmentioning

confidence: 99%

Section: Case Studymentioning

confidence: 99%

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Dynamic coal blending under sustainability concerns: a case study from the Indian steel industry

Ahmad

Firouz²,

Srivastava³

2023

IJQRM

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“…To the best of the authors' knowledge, there is a lack of information about the possibility of producing blast furnace coke with the use of a certain amount of lignite. In particular, this gap concerns the impact of lignite addition on the most important parameters currently used for coke quality evaluation-NSC (Nippon Steel Corporation), coke reactivity index (CRI) and coke strength after reaction (CSR) [37][38][39][40]. Furthermore, there is a lack of data on the impact of lignite addition on the textural and structural properties of coke, which are crucial for coke quality parameters.…”

Section: Introductionmentioning

confidence: 99%

Use of Alternative Raw Materials in Coke-Making: New Insights in the Use of Lignites for Blast Furnace Coke Production

2020

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This paper presents the results of studies on the possibility of using lignite to produce blast furnace coke. The aim of the investigation was to evaluate the influence of lignite addition (direct addition or incorporated into briquettes) on the textural, structural and quality parameters (NSC-CRI and CSR) of blast furnace coke. It was found that the introduction of lignite in briquettes (4.5% addition) allows coke to be produced that is characterized by equally high NSC parameters as for coke obtained without lignite addition for standard top-charged operation.

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“…This eventually led to a deeper understanding of the development of mechanical strength in the oven. Numerous studies on predicting the coke quality based on the coal blend properties were carried out and mathematical models to predict the same have also been proposed [3][4][5][6]. Several researchers across the globe have contributed extensively in developing novel techniques to enhance the coke quality such as addition of organic binders [7] and phenolic resins [8].…”

Section: Introductionmentioning

confidence: 99%

Prediction of carbonisation time in byproduct recovery coke ovens through gooseneck temperature measurement

Vasudharini

Dash

Singh

2018

Ironmaking & Steelmaking

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Precise prediction of the end of carbonisation possesses intangible benefits in the coke making process. The coke ovens in Tata Steel measure the raw gas temperature (at the gooseneck arrangement in the oven top) to identify the end of coking. Based on the gooseneck temperature profile, the carbonisation time is divided into active carbonisation time (ACT) and soaking time. As the soaking time is varied between 45 min and 1.5 h as per the need, the current study focusses on developing a mathematical model to predict the ACT given the coal blend properties and the operating conditions of the oven. Different statistical methods ranging from linear regression to artificial neural network (ANN) have been used to arrive at a robust model. Piece-wise linear regression and ANN have been found to out-perform the other statistical techniques. However, the ANN model is preferred in terms of the predictability of unseen data.

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Prediction of Coke CSR from Ash Chemistry of Coal Blend

Cited by 17 publications

References 7 publications

Dynamic coal blending under sustainability concerns: a case study from the Indian steel industry

Dynamic coal blending under sustainability concerns: a case study from the Indian steel industry

Use of Alternative Raw Materials in Coke-Making: New Insights in the Use of Lignites for Blast Furnace Coke Production

Prediction of carbonisation time in byproduct recovery coke ovens through gooseneck temperature measurement

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